Search (3 results, page 1 of 1)

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Abstract

Authority control is a standard practice in the library community that provides consistent, unique, and unambiguous reference to entities such as persons, places, concepts, etc. The ideal way of referring to authority records through unique identifiers is in line with the current linked data principle. When presenting a bibliographic record, the linked authority records are expanded with the authoritative information. This way, any update in the authority records will not affect the indexing of the bibliographic records. The structural information in the authority files can also be leveraged to expand the user's query to retrieve bibliographic records associated with all the variations, narrower terms or related terms. However, in many digital libraries, especially largescale aggregations such as WorldCat and Europeana, name strings are often used instead of authority record identifiers. This is also partly due to the lack of global authority records that are valid across countries and cultural heritage domains. But even when there are global authority systems, they are not applied at scale. For example, in WorldCat, only 15% of the records have DDC and 3% have UDC codes; less than 40% of the records have one or more topical terms catalogued in the 650 MARC field, many of which are too general (such as "sports" or "literature") to be useful for retrieving bibliographic records. Therefore, when a user query is based on a Dewey code, the results usually have high precision but the recall is much lower than it should be; and, a search on a general topical term returns millions of hits without being even complete. All these practices make it difficult to leverage the key benefits of authority files. This is also true for authority files that have been transformed into linked data and enriched with mapping information. There are practical reasons for using name strings instead of identifiers. One is the indexing and query response. The future infrastructure design should take the performance into account while embracing the benefit of linking instead of copying, without introducing extra complexity to users. Notwithstanding all the restrictions, we argue that largescale aggregations also bring new opportunities for better exploiting the benefits of authority records. It is possible to use machine learning techniques to automatically link bibliographic records to authority records based on the manual input of cataloguers. Text mining and visualization techniques can offer a contextual view of authority records, which in return can be used to retrieve missing or mis-catalogued records. In this talk, we will describe such opportunities in more detail.

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Abstract

This paper gives a comprehensive overview over the problem of Semantic Interoperability in the Cultural Heritage domain, with a particular focus on solutions centered around extensional, i.e., instance-based, ontology matching methods. It presents three typical scenarios requiring interoperability, one with homogenous collections, one with heterogeneous collections, and one with multi-lingual collection. It discusses two different ways to evaluate potential alignments, one based on the application of re-indexing, one using a reference alignment. To these scenarios we apply extensional matching with different similarity measures which gives interesting insights. Finally, we firmly position our work in the Cultural Heritage context through an extensive discussion of the relevance for, and issues related to this specific field. The findings are as unspectacular as expected but nevertheless important: the provided methods can really improve interoperability in a number of important cases, but they are not universal solutions to all related problems. This paper will provide a solid foundation for any future work on Semantic Interoperability in the Cultural Heritage domain, in particular for anybody intending to apply extensional methods.

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Abstract

Automatic subject prediction is a desirable feature for modern digital library systems, as manual indexing can no longer cope with the rapid growth of digital collections. It is also desirable to be able to identify a small set of entities (e.g., authors, citations, bibliographic records) which are most relevant to a query. This gets more difficult when the amount of data increases dramatically. Data sparsity and model scalability are the major challenges to solving this type of extreme multilabel classification problem automatically. In this paper, we propose to address this problem in two steps: we first embed different types of entities into the same semantic space, where similarity could be computed easily; second, we propose a novel non-parametric method to identify the most relevant entities in addition to direct semantic similarities. We show how effectively this approach predicts even very specialised subjects, which are associated with few documents in the training set and are more problematic for a classifier.